title: Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations.
abstract: Standard multinomial logit (MNL) and mixed logit (MXL) models are developed to estimate the degree of influence that bicyclist, driver, motor vehicle, geometric, environmental, and crash type characteristics have on bicyclist injury severity, classified as property damage only, possible, nonincapacitating or severe (i.e., incapacitating or fatal) injury. This study is based on 10,029 bicycleinvolved crashes that occurred in the State of Ohio from 2002 to 2008. Results of likelihood ratio tests reveal that some of the factors affecting bicyclist injury severity at intersection and non-intersection locations are substantively different and using a common model to jointly estimate impacts on severity at both types of locations may result in biased or inconsistent estimates. Consequently, separate models are developed to independently assess the impacts of various factors on the degree of bicyclist injury severity resulting from crashes at intersection and non-intersection locations. Several covariates are found to have similar impacts on injury severity at both intersection and non-intersection locations. Conversely, six variables were found to significantly influence injury severity at intersection locations but not non-intersection locations while four variables influenced bicyclist injury severity only at non-intersection locations. In crashes occurring at intersection locations, the likelihood of severe bicyclist injury increases by 14.8 percent if the bicyclist is not wearing a helmet, 82.2 percent if the motorist is under the influence of alcohol, 141.3 percent if the crash-involved motor vehicle is a van, 40.6 percent if the motor vehicle strikes the side of the bicycle, and 182.6 percent if the crash occurs on a horizontal curve with a grade. Results from non-intersection locations show the likelihood of severe injuries increases by 374.5 percent if the bicyclist is under the influence of drugs, 150.1 percent if the motorist is under the influence of alcohol, 53.5 percent if the motor vehicle strikes the side of the bicycle and 99.9 percent if the crash-involved motor vehicle is a heavy-duty truck.
authors.affiliation: The University of Akron, Civil Engineering, Auburn Science and Engineering Center, Akron, OH 44325, United States. darrenmoore@gmail.com
authors.forename: Darren N
authors.initials: DN
authors.lastname: Moore
authors.forename: William H
authors.initials: WH
authors.lastname: Schneider
authors.forename: Peter T
authors.initials: PT
authors.lastname: Savolainen
authors.forename: Mohamadreza
authors.initials: M
authors.lastname: Farzaneh
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000435:Alcoholic Intoxication / Q000453:epidemiology / Q000517:prevention & control; D001642:Bicycling / Q000293:injuries*; D002648:Child; D003430:Cross-Sectional Studies; D004779:Environment Design; D005260:Female; D006260:Head Protective Devices / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D018986:Motor Vehicles / Q000706:statistics & numerical data; D009104:Multiple Trauma / Q000453:epidemiology* / Q000517:prevention & control*; D018570:Risk Assessment / Q000706:statistics & numerical data; D019966:Substance-Related Disorders / Q000453:epidemiology / Q000517:prevention & control; D055815:Young Adult